SoroushVahidi/scidocs huggingface dataset:
Privacy Aspects of Recommender Systems
(2015)

The popularity of online recommender systems has soared; they are deployed in numerous websites and gather tremendous amounts of user data that are necessary for the recommendation purposes. This data, however, may pose a severe threat to user privacy, if accessed by untrusted parties or used inappropriately. Hence, it is of paramount importance for recommender system designers and service providers to find a sweet spot, which allows them to generate accurate recommendations and guarantee the privacy of their users. In this chapter we overview the state of the art in privacy enhanced recommendations. We analyze the risks to user privacy imposed by recommender systems, survey the existing solutions, and discuss the privacy implications for the users of the recommenders. We conclude that a considerable effort is still required to develop practical recommendation solutions that provide adequate privacy guarantees, while at the same time facilitating the delivery of high-quality recommendations to their users. Arik Friedman NICTA, Australia, e-mail: arik.friedman@nicta.com.au Bart Knijnenburg UC Irvine, USA, e-mail: bart.k@uci.edu Kris Vanhecke iMinds Ghent University, Belgium, e-mail: kris.vanhecke@intec.ugent.be Luc Martens iMinds Ghent University, Belgium, e-mail: luc.martens@intec.ugent.be Shlomo Berkovsky CSIRO, Australia, e-mail: shlomo.berkovsky@csiro.au 1 Accepted to be included in the 2nd edition of the Recommender Systems handbook. Please do not share publicly, and consult the authors before citing this chapter.
ArgumenText: Searching for Arguments in Heterogeneous Sources
(2018)

Argument mining is a core technology for enabling argument search in large corpora. However, most current approaches fall short when applied to heterogeneous texts. In this paper, we present an argument retrieval system capable of retrieving sentential arguments for any given controversial topic. By analyzing the highest-ranked results extracted from Web sources, we found that our system covers 89% of arguments found in expert-curated lists of arguments from an online debate portal, and also identifies additional valid arguments.
A qualitative study of Ragnarök Online private servers: in-game sociological issues
(2010)

In the last decade, online games have garnered much attention as more and more players gather on game servers. In parallel, communities of illegal private server players and administrators have spread and might host hundreds of thousands of players. To study the Korean online game Ragnarök Online, we conducted interviews to collect qualitative data on two private servers as well as on the official French server for the game. This paper discusses some of the reasons why Ragnarök Online private servers might attract players and how examining private servers' characteristics could help improve official game servers.
A 28GHz SiGe BiCMOS phase invariant VGA
(2016)

The paper describes a technique to design a phase invariant variable gain amplifier (VGA). Variable gain is achieved by varying the bias current in a BJT, while the phase variation is minimized by designing a local feedback network such that the applied base to emitter voltage has a bias-dependent phase variation which compensates the inherent phase variation of the transconductance. Two differential 28GHz VGA variants based on these principles achieve <;5° phase variation over 8dB and 18dB of gain control range, respectively, with phase invariance maintained over PVT. Implemented in GF 8HP BiCMOS technology, the VGAs achieve 18dB nominal gain, 4GHz bandwidth, and IP1dB > -13dBm while consuming 35mW.
Bacteriocins: developing innate immunity for food.
(2005)

Bacteriocins are bacterially produced antimicrobial peptides with narrow or broad host ranges. Many bacteriocins are produced by food-grade lactic acid bacteria, a phenomenon which offers food scientists the possibility of directing or preventing the development of specific bacterial species in food. This can be particularly useful in preservation or food safety applications, but also has implications for the development of desirable flora in fermented food. In this sense, bacteriocins can be used to confer a rudimentary form of innate immunity to foodstuffs, helping processors extend their control over the food flora long after manufacture.
DPABI: Data Processing & Analysis for (Resting-State) Brain Imaging
(2016)

Brain imaging efforts are being increasingly devoted to decode the functioning of the human brain. Among neuroimaging techniques, resting-state fMRI (R-fMRI) is currently expanding exponentially. Beyond the general neuroimaging analysis packages (e.g., SPM, AFNI and FSL), REST and DPARSF were developed to meet the increasing need of user-friendly toolboxes for R-fMRI data processing. To address recently identified methodological challenges of R-fMRI, we introduce the newly developed toolbox, DPABI, which was evolved from REST and DPARSF. DPABI incorporates recent research advances on head motion control and measurement standardization, thus allowing users to evaluate results using stringent control strategies. DPABI also emphasizes test-retest reliability and quality control of data processing. Furthermore, DPABI provides a user-friendly pipeline analysis toolkit for rat/monkey R-fMRI data analysis to reflect the rapid advances in animal imaging. In addition, DPABI includes preprocessing modules for task-based fMRI, voxel-based morphometry analysis, statistical analysis and results viewing. DPABI is designed to make data analysis require fewer manual operations, be less time-consuming, have a lower skill requirement, a smaller risk of inadvertent mistakes, and be more comparable across studies. We anticipate this open-source toolbox will assist novices and expert users alike and continue to support advancing R-fMRI methodology and its application to clinical translational studies.
On the Traveling Salesman Problem with Simple Temporal Constraints
(2013)

Many real-world applications require the successful combination of spatial and temporal reasoning. In this paper, we study the general framework of the Traveling Salesman Problem with Simple Temporal Constraints. Representationally, this framework subsumes the Traveling Salesman Problem, Simple Temporal Problems, as well as many of the frameworks described in the literature. We analyze the theoretical properties of the combined problem providing strong inapproximability results for the general problem, and positive results for
A Survey on Detection of Reasons Behind Infant Cry Using Speech Processing
(2018)

Infant cry analysis is necessary to detect the reasons behind the cry. Infants can communicate through their cry only. They express their physical and emotional needs through their cry. The crying of an infant can be stopped if the reason behind their cry is known. Therefore, identifying the reason behind infant cry is very essential for medical follow up. In this paper, a literature survey is done to study, analyze, compare the existing approaches and their limitations. This survey helped us to propose a new approach for detecting the reasons behind Infant's cry. The proposed model will use input as the cry signal and from these signal patterns the unique features are extracted using MFCCs, LPCCs and Pitch, etc. These features will help discriminating the patterns from one another. These extracted features will be used to train Neural Network Multilayer classifier. This classifier will be used to identify multiple classes of reasons behind infant cry such as hunger, pain, sleep, discomfort, etc. Efforts will be made for achieving good accuracy results through this approach.
Convolutional neural networks: an illustration in TensorFlow
(2016)

C onvolutional neural networks (CNNs) have interesting and widely varied architectures catering to the requirement of learning features from three dimensional and structured data volumes (with a particular example being images, whether single or multi-channel). However, there are certain design considerations common to all CNN architectures that enable us to craft correct implementations of the architectures suited to our objectives. It is more than just mathematical calculations—it's an art in itself! Thus, in the present tutorial, we'll put our knowledge of CNNs and their distinct layers in practice by making use of Google's open-source machine learning (ML) platform, TensorFlow. The tutorial is fairly simple and follows the steps detailed online [1] in light of added insights, which can be assimilated from the basics of CNN architectural concepts. The TensorFlow platform was released in November 2015, in a climate of stiff competition from well-established deep learning and ML packages such as Torch and Theano. (The performance comparisons between the packages goes beyond the scope of this piece). The platform has been undergoing constant improvements since its release, thanks in part to a nascent community of contributors. TensorFlow packs quite a punch owing to its significant positive characteristics. Firstly, the package's open source nature makes it transparent, customizable, and extensible by the end user. Secondly, the documentation support is rich and elaborate, which is a critical factor in accelerating user adoption. Lastly, the fact that the package has been backed by Google, with some of the best ML and AI researchers being among its active contributors, is the cherry on top. Additionally it's compatibility with newer versions of Python (version 3.3 and above), optional GPU utilization, parallelizable execution, and visualization support via TensorBoard further enrich its impressive repertoire. Its Achilles heel—which has prevented widespread adoption—is its unavailability on the Windows OS (TensorFlow is only available on Mac OSX and Linux platforms), and its non-operability in distributed environments, as of now (it's bound to change soon). The question as to why the MNIST dataset [2] was chosen for this tutorial is a very pertinent one. The answer is the handwritten digits dataset has been ubiquitous since its release in 1998 by Yann LeCunn [3]. Having since become the textbook dataset for neural network classification tasks, it is a good choice in our context of getting acquainted with CNNs. The dataset consists of 60,000 training examples and 10,000 test examples, each being a grayscale …
TextCatcher: a method to detect curved and challenging text in natural scenes
(2016)

In this paper, we propose a text detection algorithm which is hybrid and multi-scale. First, it relies on a connected component-based approach: After the segmentation of the image, a classification step using a new wavelet descriptor spots the letters. A new graph modeling and its traversal procedure allow to form candidate text areas. Second, a texture-based approach discards the false positives. Finally, the detected text areas are precisely cut out and a new binarization step is introduced. The main advantage of our method is that few assumptions are put forward. Thus, “challenging texts” like multi-sized, multi-colored, multi-oriented or curved text can be localized. The efficiency of TextCatcher has been validated on three different datasets: Two come from the ICDAR competition, and the third one contains photographs we have taken with various daily life texts. We present both qualitative and quantitative results.
Put your money where your mouth is! Explaining collective action tendencies through group-based anger and group efficacy.
(2004)

Insights from appraisal theories of emotion are used to integrate elements of theories on collective action. Three experiments with disadvantaged groups systematically manipulated procedural fairness (Study 1), emotional social support (Study 2), and instrumental social support (Study 3) to examine their effects on collective action tendencies through group-based anger and group efficacy. Results of structural equation modeling showed that procedural fairness and emotional social support affected the group-based anger pathway (reflecting emotion-focused coping), whereas instrumental social support affected the group efficacy pathway (reflecting problem-focused coping), constituting 2 distinct pathways to collective action tendencies. Analyses of the means suggest that collective action tendencies become stronger the more fellow group members "put their money where their mouth is." The authors discuss how their dual pathway model integrates and extends elements of current approaches to collective action.
Taxonomy of intrusion risk assessment and response system
(2014)

In recent years, we have seen notable changes in the way attackers infiltrate computer systems compromising their functionality. Research in intrusion detection systems aims to reduce the impact of these attacks. In this paper, we present a taxonomy of intrusion response systems (IRS) and Intrusion Risk Assessment (IRA), two important components of an intrusion detection solution. We achieve this by classifying a number of studies published during the last two decades . We discuss the key features of existing IRS and IRA. We show how characterizing security risks and choosing the right countermeasures are an important and challenging part of designing an IRS and an IRA. Poorly designed IRS and IRA may reduce network performance and wrongly disconnect users from a network. We propose techniques on how to address these challenges and highlight the need for a comprehensive defense mechanism approach. We believe that this taxonomy will open up interesting areas for future research in the growing field of intrusion risk assessment and response systems.
Segmentation of Medical Ultrasound Images Using Convolutional Neural Networks with Noisy Activating Functions
(2016)

The attempts to segment medical ultrasound images have had limited success than the attempts to segment images from other medical imaging modalities. In this project, we attempt to segment medical ultrasound images using convolutional neural networks (CNNs) with a group of noisy activation functions which have recently been demonstrated to improve the performance of neural networks. We report on the segmentation results using a U-Net-like CNN with noisy rectified linear unit (NReLU) functions, noisy hard sigmoid (NHSigmoid) functions, and noisy hard tanh (NHTanh) function on a small data set.
GSA: A Gravitational Search Algorithm
(2009)

In recent years, various heuristic optimization methods have been developed. Many of these methods are inspired by swarm behaviors in nature. In this paper, a new optimization algorithm based on the law of gravity and mass interactions is introduced. In the proposed algorithm, the searcher agents are a collection of masses which interact with each other based on the Newtonian gravity and the laws of motion. The proposed method has been compared with some well-known heuristic search methods. The obtained results confirm the high performance of the proposed method in solving various nonlinear functions. 2009 Elsevier Inc. All rights reserved.
Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion
(2012)

Here, we demonstrate that subject motion produces substantial changes in the timecourses of resting state functional connectivity MRI (rs-fcMRI) data despite compensatory spatial registration and regression of motion estimates from the data. These changes cause systematic but spurious correlation structures throughout the brain. Specifically, many long-distance correlations are decreased by subject motion, whereas many short-distance correlations are increased. These changes in rs-fcMRI correlations do not arise from, nor are they adequately countered by, some common functional connectivity processing steps. Two indices of data quality are proposed, and a simple method to reduce motion-related effects in rs-fcMRI analyses is demonstrated that should be flexibly implementable across a variety of software platforms. We demonstrate how application of this technique impacts our own data, modifying previous conclusions about brain development. These results suggest the need for greater care in dealing with subject motion, and the need to critically revisit previous rs-fcMRI work that may not have adequately controlled for effects of transient subject movements.
Modeling regional and psychophysiologic interactions in fMRI: the importance of hemodynamic deconvolution
(2003)

The analysis of functional magnetic resonance imaging (fMRI) time-series data can provide information not only about task-related activity, but also about the connectivity (functional or effective) among regions and the influences of behavioral or physiologic states on that connectivity. Similar analyses have been performed in other imaging modalities, such as positron emission tomography. However, fMRI is unique because the information about the underlying neuronal activity is filtered or convolved with a hemodynamic response function. Previous studies of regional connectivity in fMRI have overlooked this convolution and have assumed that the observed hemodynamic response approximates the neuronal response. In this article, this assumption is revisited using estimates of underlying neuronal activity. These estimates use a parametric empirical Bayes formulation for hemodynamic deconvolution.
Common regions of the human frontal lobe recruited by diverse cognitive demands
(2000)

Though many neuroscientific methods have been brought to bear in the search for functional specializations within prefrontal cortex, little consensus has emerged. To assess the contribution of functional neuroimaging, this article reviews patterns of frontal-lobe activation associated with a broad range of different cognitive demands, including aspects of perception, response selection, executive control, working memory, episodic memory and problem solving. The results show a striking regularity: for many demands, there is a similar recruitment of mid-dorsolateral, mid-ventrolateral and dorsal anterior cingulate cortex. Much of the remainder of frontal cortex, including most of the medial and orbital surfaces, is largely insensitive to these demands. Undoubtedly, these results provide strong evidence for regional specialization of function within prefrontal cortex. This specialization, however, takes an unexpected form: a specific frontal-lobe network that is consistently recruited for solution of diverse cognitive problems.
Prefrontal cortical function and anxiety: controlling attention to threat-related stimuli
(2004)

Threat-related stimuli are strong competitors for attention, particularly in anxious individuals. We used functional magnetic resonance imaging (fMRI) with healthy human volunteers to study how the processing of threat-related distractors is controlled and whether this alters as anxiety levels increase. Our work builds upon prior analyses of the cognitive control functions of lateral prefrontal cortex (lateral PFC) and anterior cingulate cortex (ACC). We found that rostral ACC was strongly activated by infrequent threat-related distractors, consistent with a role for this area in responding to unexpected processing conflict caused by salient emotional stimuli. Participants with higher anxiety levels showed both less rostral ACC activity overall and reduced recruitment of lateral PFC as expectancy of threat-related distractors was established. This supports the proposal that anxiety is associated with reduced top-down control over threat-related distractors. Our results suggest distinct roles for rostral ACC and lateral PFC in governing the processing of task-irrelevant, threat-related stimuli, and indicate reduced recruitment of this circuitry in anxiety.
Other minds in the brain: a functional imaging study of “theory of mind” in story comprehension
(1995)

The ability of normal children and adults to attribute independent mental states to self and others in order to explain and predict behaviour ("theory of mind") has been a focus of much recent research. Autism is a biologically based disorder which appears to be characterised by a specific impairment in this "mentalising" process. The present paper reports a functional neuroimaging study with positron emission tomography in which we studied brain activity in normal volunteers while they performed story comprehension tasks necessitating the attribution of mental states. The resultant brain activity was compared with that measured in two control tasks: "physical" stories which did not require this mental attribution, and passages of unlinked sentences. Both story conditions, when compared to the unlinked sentences, showed significantly increased regional cerebral blood flow in the following regions: the temporal poles bilaterally, the left superior temporal gyrus and the posterior cingulate cortex. Comparison of the "theory of mind" stories with "physical" stores revealed a specific pattern of activation associated with mental state attribution: it was only this task which produced activation in the medial frontal gyrus on the left (Brodmann's area 8). This comparison also showed significant activation in the posterior cingulate cortex. These surprisingly clear-cut findings are discussed in relation to previous studies of brain activation during story comprehension. The localisation of brain regions involved in normal attribution of mental states and contextual problem solving is feasible and may have implication for the neural basis of autism.
Daemo: A Self-Governed Crowdsourcing Marketplace
(2015)

Crowdsourcing marketplaces provide opportunities for autonomous and collaborative professional work as well as social engagement. However, in these marketplaces, workers feel disrespected due to unreasonable rejections and low payments, whereas requesters do not trust the results they receive. The lack of trust and uneven distribution of power among workers and requesters have raised serious concerns about sustainability of these marketplaces. To address the challenges of trust and power, this paper introduces Daemo, a self-governed crowdsourcing marketplace. We propose a prototype task to improve the work quality and open-governance model to achieve equitable representation. We envisage Daemo will enable workers to build sustainable careers and provide requesters with timely, quality labor for their businesses.
Discriminative shape from shading in uncalibrated illumination
(2015)

Estimating surface normals from just a single image is challenging. To simplify the problem, previous work focused on special cases, including directional lighting, known reflectance maps, etc., making shape from shading impractical outside the lab. To cope with more realistic settings, shading cues need to be combined and generalized to natural illumination. This significantly increases the complexity of the approach, as well as the number of parameters that require tuning. Enabled by a new large-scale dataset for training and analysis, we address this with a discriminative learning approach to shape from shading, which uses regression forests for efficient pixel-independent prediction and fast learning. Von Mises-Fisher distributions in the leaves of each tree enable the estimation of surface normals. To account for their expected spatial regularity, we introduce spatial features, including texton and silhouette features. The proposed silhouette features are computed from the occluding contours of the surface and provide scale-invariant context. Aside from computational efficiency, they enable good generalization to unseen data and importantly allow for a robust estimation of the reflectance map, extending our approach to the uncalibrated setting. Experiments show that our discriminative approach outperforms state-of-the-art methods on synthetic and real-world datasets.